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    This study introduces new coding patterns for compressive X-ray Compton backscattering imaging (CXBI) to improve security scans. Model-based and data-driven methods designed optimal patterns, outperforming random ones for better image quality and reduced radiation dose.

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    Area of Science:

    • Medical imaging
    • Computational imaging
    • X-ray physics

    Background:

    • Compressive X-ray Compton backscattering imaging (CXBI) is a novel technique for security inspections, reducing radiation dose.
    • It utilizes coded illumination patterns and single-pixel imaging principles.
    • Optimal coding patterns for CXBI have not been previously explored.

    Purpose of the Study:

    • To analyze the sensing matrix of CXBI.
    • To develop model-based and data-driven solutions for optimal coding patterns.
    • To maximize the quality of reconstructed images in CXBI.

    Main Methods:

    • Analysis of the CXBI sensing matrix using features like transmittance and mutual coherence.
    • Model-based approach using gradient descent, ADMM, and BM3D denoising.
    • Data-driven approach employing residual U-blocks for training and reconstruction with Geant-4 simulated noise.
    • Testing patterns with Geant-4 Application for Tomographic Emissions (GATE).

    Main Results:

    • Both model-based and data-driven designed codes significantly outperform random codes.
    • The data-driven approach yields superior image quality metrics.
    • Optimal patterns were identified and validated under realistic simulation conditions.

    Conclusions:

    • Designed coding patterns enhance CXBI performance.
    • Data-driven solutions offer the highest quality reconstructions.
    • This research paves the way for improved security inspection techniques using CXBI.